Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (Key Components) for MetaTrader 5
In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (S3CE-Net) for MetaTrader 5
We invite you to explore the S3CE-Net framework and its SSAM and STFS mechanisms, which accurately process spiking events while taking causality into account. The model is lightweight, parallel, and capable of identifying complex relationships across time and space
Neural Networks in Trading: Training Deep Spiking Models (Conclusion) for MetaTrader 5
The article demonstrates a practical implementation of the SEW ResNet framework using MQL5, with a focus on its applied use in trading. The Double Bottleneck makes it possible to simultaneously analyze univariate streams and inter-channel dependencies without losing gradients during training
Neural Networks in Trading: Training Deep Spiking Models (Spike Integration) for MetaTrader 5
The article presents a practical implementation of the key components of the SEW-ResNet framework using MQL5. The use of dynamic arrays and spiking mechanisms enables flexible model architecture design and efficient processing of financial time series. The proposed solutions demonstrate how
Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet) for MetaTrader 5
We invite you to explore the SEW-ResNet framework, which allows you to build deep spiking models without degradation issues and with effective gradient management. In this article, we demonstrate how to implement a basic spiking neuron and its algorithms using MQL5
Neural Networks in Trading: From Transformers to Spiking Neurons (Conclusion) for MetaTrader 5
Neural networks are already changing the way we analyze markets, and new architectures are opening up even more possibilities. In this article, we wrap up our work with the SpikingBrain framework, which opens up new possibilities for us
Neural Networks in Trading: From Transformers to Spiking Neurons (Key Components) for MetaTrader 5
We present to the reader an implementation of the SpikingBrain framework’s approaches based on recurrent linear attention with gates, discussed in detail in this article. Forward pass, gradient propagation, and weight update algorithms ensure efficient processing of financial time series and enable
Neural Networks in Trading: From Transformers to Spiking Neurons (SpikingBrain) for MetaTrader 5
The SpikingBrain framework demonstrates a unique approach to data processing: neurons respond only to significant events, effectively filtering out noise. Its event-driven architecture reduces computational costs while preserving key information about price movements. Adaptive thresholds and the
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Conclusion) for MetaTrader 5
The article provides a detailed examination of the integration of the ST-Expert framework's approaches into the Extralonger architecture, which enables the simultaneous analysis of temporal and spatial representations of data. The results of testing on real historical data are presented
Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Attention Modules) for MetaTrader 5
In this article, we continue implementing the ST-Expert framework approaches, focusing on the practical aspects of applying them using MQL5. Earlier, we examined the theoretical foundations and key components of the model; now we move on to working directly with graph attention algorithms and local